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For this purpose we employ the Significance Analysis of Microarrays (SAM) algorithm [ 17], which performs multiple gene specific t-tests on permutations of the initial dataset, in order to account for the initially enormous number of input genes.
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In these within-dataset experiments, the initial set of top 50 markers have been selected using the entire dataset, in order to reduce the effect of sensitivity in marker selection when comparing different pathway-based methods.
We can add examples from the dataset, in order of similarity to the target audio which will weaken the initial constraint on which parts of the speech manifold can be traversed.
Two precipitation flags are consistently used in all three versions of the dataset in order to discriminate the precipitation phase and intensity.
Prepare to describe your dataset in order for it to be discoverable in the repository.
Thirdly, more methods [29] can be used to filter the training dataset in order to make the dataset trustworthy.
Synthetic dataset In order to evaluate completion time performance on Big Data, we created a synthetic dataset via a Big Data generator.
We calculated Spearman's rank-order correlation, rs, for each dataset in order to test for potential publication bias [57] [59].
KEGG dataset in order of average gene abundance.
This initial dataset, shown in Additional file 1, is the starting point of the subsequent analysis.
Furthermore, we combined dataset 1 and dataset 2 together as dataset 3 in order to compare the outcomes from de novo assembly using different datasets.
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